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There has been significant literature regarding the way product reviewsentiment affects brand loyalty. Intrigued by how natural language influencesconsumer choice, we were motivated to examine whether an AirBnb host'soccupancy rate (how often their listing is booked out of the days theyindicated their listing was available) can be determined by the perceivedsentiment and length of their description summary. Our main goal, moregenerally, was to determine which features, including (but not limited to)sentiment and description length, most influence a host's occupancy rate. Wedefine sentiment score through a natural language algorithm process, based onthe AFINN dictionary. Using AirBnB data on New York City, our hypothesis isthat higher sentiment scores (more positive descriptions) and longer summarylength lead to higher occupancy rates. Our results show that while longersummary length may positively influence occupancy rates, more positive summarydescriptions have no effect. Instead, we find that other factors such as numberof reviews and number of amenities, in addition to summary length, are betterindicators of occupancy rate.

The Impact of an AirBnb Host's Listing Description 'Sentiment' and Length On Occupancy Rates